Machine Learning Fundamentals · Model evaluation

Classification evaluation metrics

Accuracy alone can lie; precision, recall, F1, and ROC-AUC each capture a different way a classifier can be right or wrong.

Also known as: precision, recall, F1, ROC-AUC, confusion matrix.

Start a lesson on Classification evaluation metrics

Learn first

• Logistic regression: A linear model adapted for classification by squashing its output into a probability.

Leads to

• Class imbalance handling: Techniques for when one class vastly outnumbers another, since accuracy alone becomes meaningless (predicting the majority class always "wins").

Related topics

• Class imbalance handling: Techniques for when one class vastly outnumbers another, since accuracy alone becomes meaningless (predicting the majority class always "wins").